Grey uncertain prediction of carbon emissions peak from thirty-one provinces and municipalities in China

ABSTRACT

In recent years, China’s air quality problem is very serious. Among them, carbon dioxide emissions have been high, which has caused a series of ecological and environmental problems. In order to deeply study the relevant problems, this paper selects the information priority damping gray model to study and predict the carbon emissions of thirty-one provinces and municipalities in China. The model can give priority to new information and adjust the prediction trend, the accuracy of the model is verified by several cases. Through the data collection and data analysis of relevant provinces and cities, it is concluded that a total of twenty-one provinces and municipalities can reach the carbon peak in 2030, of which the carbon dioxide emission of Beijing is expected to drop to 51.3774 million tons by 2030. Ten provinces will not be able to reach the carbon peak by 2030. Among them, by 2030 the carbon dioxide emission of Jiangsu Province is expected to increase to 84.4668 million tons, but the growth rate is declining. The research results can be used as a reference for relevant departments.

KEYWORDS:

• Carbon peak
• carbon dioxide
• prediction
• China
• information priority damping grey model,

Acknowledgments

The relevant researches are supported by the National Natural Science Foundation of China (71871084, U20A20316), Young talent support scheme of Hebei Province (360-0803-YBN-7U2C), the key research project in humanity and social science of Hebei Education Department (ZD202211) and the Natural Science Foundation of Hebei Province (E2020402074).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the National Natural Science Foundation of China (71871084, U20A20316), Young talent support scheme of Hebei Province (360-0803-YBN-7U2C), the key research project in humanity and social science of Hebei Education Department (ZD202211) and the Natural Science Foundation of Hebei Province (E2020402074).